Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient-UCBV: An Almost Optimal Algorithm Using Variance Estimates
Authors: Subhojyoti Mukherjee, K. P. Naveen, Nandan Sudarsanam, Balaraman Ravindran
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through an extensive numerical study we show that EUCBV significantly outperforms the popular UCB variants (like MOSS, OCUCB, etc.) as well as Thompson sampling and Bayes-UCB algorithms. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, Indian Institute of Technology Madras 2Department of Electrical Engineering, Indian Institute of Technology Tirupati 3Department of Management Studies, Indian Institute of Technology Madras 4 Robert Bosch Centre for Data Science and AI (RBC-DSAI), Indian Institute of Technology Madras |
| Pseudocode | Yes | Algorithm 1 EUCBV |
| Open Source Code | No | The paper does not provide a link or explicit statement about the open-sourcing of the EUCBV algorithm's code. It mentions code for other algorithms: 'The implementation for KLUCB, Bayes-UCB and DMED were taken from Cappe, Garivier, and Kaufmann (2012)'. |
| Open Datasets | No | The paper describes experiments using synthetically generated data (e.g., '20 Bernoulli distributed arms', '100 arms involving Gaussian reward distributions') rather than named public datasets with explicit access information. |
| Dataset Splits | No | The paper does not explicitly describe train/validation/test dataset splits. For multi-armed bandit problems, the algorithm learns sequentially rather than on pre-split datasets in the manner of supervised learning. |
| Hardware Specification | No | No specific hardware (e.g., GPU/CPU models, memory) used for running the experiments is mentioned in the paper. |
| Software Dependencies | No | The paper mentions that implementations for some baseline algorithms were taken from a citation ('Cappe, Garivier, and Kaufmann (2012)') but does not list specific software dependencies with version numbers for their own algorithm or experimental setup. |
| Experiment Setup | Yes | The parameters of EUCBV algorithm for all the experiments are set as follows: ψ = T K2 and ρ = 0.5 (as in Corollary 1). Experiment-1 (Bernoulli with uniform gaps): This experiment is conducted to observe the performance of EUCBV over a short horizon. The horizon T is set to 60000. The testbed comprises of 20 Bernoulli distributed arms with expected rewards of the arms as r1:19 = 0.07 and r 20 = 0.1... |